The shallow features extracted by the traditional artificial intelligence algorithm-based damage identification methods pose low sensitivity and ignore timing characteristics of vibration signals. Thus, this study uses high-dimensional feature extraction advantages convolutional neural networks (CNNs) time series modeling capability long short-term memory (LSTM) to identify long-span bridges. F...

The purpose of this study is to evaluate the long-term vertical deformations of segmented pre-tensioned concrete bridges by a new approach. It provides a practical and reliable method for calculating the amount of long-term deformation based on creep and shrinkage in segmented prestress bridges. There are various relationships for estimating the creep and shrinkage of concrete. The analytical r...

In the fall of 1998, the Federal Highway Administration of the U.S. Department of Transportation initiated a series of studies related to the seismic performance of the U.S. national highway system. The research conducted under this study is intended to provide improved tools for evaluating and assessing the social costs and impacts of earthquakes on highway systems and bridges, and to reduce t...

Reliable estimation of wind-induced displacement responses long-span bridges is critical to evaluating their wind-resistant performance. In this study, two Bayesian approaches, generalized linear model (BGLM) and sparse learning (SBL), are proposed for characterizing the lateral with structural health monitoring (SHM) data. They fully model-free data-driven preferable reckoning total intended p...